Higher Order Bayesian Networks, Exactly - POPL 2024
Explore a higher-order programming language for Bayesian networks, combining advanced techniques from linear logic, intersection types, and rewriting theory for efficient probabilistic modeling and inference.
Turning Probabilistic Reasoning into Programming - 2003
Explore IBAL, a general-purpose probabilistic modeling language encompassing various representation methods for reasoning under uncertainty.
CrowdSource - Crowd Trained Machine Learning Model for Malware Capability Detection
Innovative machine learning tool for automated malware capability detection, trained on web technical documents. Provides probabilistic and traceable output for rapid reverse engineering of active malware variants.
Deep Probabilistic Modelling with Pyro
Explore deep probabilistic modeling with Pyro, combining probabilistic methods and neural networks for better uncertainty handling and decision-making in machine learning applications.
Vulnerability Assessment of Substation Using Bayesian Attack Graph
Explore advanced techniques for assessing substation vulnerabilities through Bayesian attack graphs, focusing on network security and risk analysis methodologies in power infrastructure.
Revisiting Bayesian Network Learning with Small Vertex Cover
Explore Bayesian network structure learning with bounded vertex cover, analyzing complexity bounds and hardness results for optimization and counting problems.
Probabilistic Circuits - Lecture on Advanced AI Techniques
Explore probabilistic circuits with YooJung Choi, delving into advanced AI concepts at the intersection of symbolic methods and deep learning.
A View in Old and New Machine Learning
Explore old and new machine learning approaches, comparing hierarchical probabilistic models with deep neural networks and examining their unique capabilities and potential combinations.
Probabilistic Models of Relational Domains
Explore probabilistic models for relational domains with Stanford's Daphne Koller, covering advanced techniques for representing and reasoning about complex, interconnected data structures.
Probabilistic Circuits
Explore probabilistic circuits with Antonio Vergari, covering foundational concepts and advanced applications in AI and machine learning.
Introduction to Probabilistic Models - Probabilistic AI School
Explore probabilistic models, their applications, and fundamental concepts in this comprehensive introduction by Silja Renooij at the Nordic Probabilistic AI School.
Introduction to Probabilistic Models - Lecture 1
Explore probabilistic models, their applications, and fundamental concepts in this comprehensive introduction by Silja Renooij at the Nordic Probabilistic AI School.
How to Benefit from Uncertainty: An Introduction to Paraconsistent Bayesian Update
Explore how paraconsistent and paracomplete logics enhance Bayesian reasoning, offering new approaches to handle contradictory evidence and uncertainty in scientific and AI applications.